Machinability in terms of quality and productivity is of great concern in a competitive market. CNC milling performance is evaluated in terms of surface roughness, material removal rate, taper and facing. CNC machining is done initially in a number of passes an final end milling is done in a single pass. The most common machining parameters are cutting speed, feed, depth of cut. To have a balance between productivity and quality, the machining parameters need to be optimised.
High speed CNC milling process needs to balance between productivity and quality of the end product. While quality is measured in terms of surface finish, the productivity is measured in terms of material removal rate. Good machinable materials acquire a smooth finish as they can be easily cut with less power. They also cause minimum damage to the tool. If the material has improved material properties then it’s machinablity becomes difficult. Hence improving machinability without sacrificing performance is a challenge.
Predicting the optimal parameters for CNC milling is difficult as the milling process depends on several factors. The important factors relative to the material include the thermal conductivity, toughness, chemical properties and the microstructure of the material. The other important factors are the geometry of the cutting tool and the parameters of CNC milling process.
The aim of this research work is to
•Find the important factors that characterise the best performance of the high speed CNC milling process.
•Using combined Genetic Algorithm and Artificial neutral networks techniques to optimise these high speed CNC milling parameters by identifying the correlation between the factors like feed, depth of cutting and cutting speed.